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Artificial intelligence (AI) architecture - Azure Architecture Center | Microsoft Learn

Explore ideas about​

  • Document processing
    • Content tagging with NLP
    • Knowledge mining for customer feedback
    • Large-scale custom NLP
  • Image processing
    • Image classification with CNNs
    • Retail assistant with visual capabilities
    • Visual assistant
    • Vision classifier model
  • Audio processing
    • Keyword digital text processing
  • Predictive analytics
    • Customer churn prediction
    • Personalized offers
    • Marketing optimization
    • Personalized marketing solutions
  • Chat bots
    • Search and query a knowledge base
  • AI at the edge
    • AI at the edge with Azure Stack Hub
    • Disconnected AI at the edge with Azure Stack Hub
    • Video ingestion and object detection on the edge
  • Document enrichment
    • AI enrichment with Cognitive Search
  • MLOps
    • Model deployment to AKS
    • Orchestrate MLOps with Azure Databricks
    • Deploy AI and ML at the edge
    • Many models ML with Spark
    • Many models with Machine Learning
  • Other ideas
    • Azure Machine Learning architecture
    • Autonomous systems
    • Data science and machine learning

Design architectures​

  • Chat bots
    • Baseline end-to-end chat with OpenAI
  • Document processing
    • Automate document classification
    • Automate document processing
    • Automate PDF form processing
    • Build custom document processing models
    • Multiple indexers with Azure Cognitive Search
  • Video and image classification
    • Automate video analysis
    • Image classification
  • Audio processing
    • Speech transcription pipeline
    • Extract and analyze call center data
  • Predictive analytics
    • Determine customer lifetime and churn
  • Batch scoring
    • Batch scoring for deep learning
    • Batch scoring with Python
    • Batch scoring with R
    • Batch scoring with Spark on Databricks
  • Recommendations
  • Monitoring
    • Monitor OpenAI models
  • Regulatory
    • Secure research for regulated data

Apply guidance​

  • Machine learning options
  • Document processing
    • OpenAI GPT-3 summarization
    • Build language model pipelines
  • Audio processing
    • Custom speech-to-text overview
    • Custom speech-to-text
    • Conversation summarization
  • MLOps
    • Machine learning operations (MLOps) v2
    • MLOps for Python models
    • Network security for MLOps
    • MLOps maturity model
    • Upscale ML lifecycle with MLOps
  • Team Data Science Process
    • Overview
    • Lifecycle
      • Overview
        1. Business understanding
        1. Data acquisition and understanding
        1. Modeling
        1. Deployment
        1. Customer acceptance
    • Roles and tasks
      • Overview
      • Group manager
      • Team lead
      • Project lead
      • Individual contributor
    • Development
      • Agile development
      • Collaborative coding with Git
      • Execute data science tasks
      • Code testing
      • Track progress
    • Operationalization
      • DevOps - CI/CD
    • Training
      • For data scientists
    • How To
      • Set up data science environments
        • Environment setup
        • Platforms and tools
      • Analyze business needs
        • Identify your scenario
      • Acquire and understand data
        • Ingest data
          • Overview
          • Move to/from Blob storage
            • Overview
            • Use Storage Explorermove-data-to-azure-blob-using-azure-storage-explorer.md
            • Use SSIS
          • Move to SQL on a VM
          • Move to Azure SQL Database
          • Move to Hive tables
          • Move to SQL partitioned tables
          • Move from on-premises SQL
        • Explore and visualize data
          • Prepare data
          • Explore data
            • Overview
            • Explore Azure Blob Storage
          • Sample data
            • Overview
            • Use Blob Storage
            • Use SQL Server
          • Process data
            • Access with Python
            • Use Azure Data Lake
            • Use SQL VM
            • Use data pipeline
            • Use Spark
            • Use Scala and Spark
      • Develop models
        • Engineer features
          • Overview
      • Deploy models in production
      • Build and deploy a model using Azure Synapse Analytics

OpenAI​

  • Explore ideas about
    • Search and query a knowledge base
  • Design architectures
    • Baseline end-to-end chat with OpenAI
    • Extract and analyze call center data
    • Monitor OpenAI models
  • Apply guidance
    • Build language model pipelines
    • OpenAI GPT-3 summarization
    • Conversation summarization